Text only print
Full screen print
Tokyo Probability Seminar
Seminar information archive ～06/11｜Next seminar｜Future seminars 06/12～
| Date, time & place | Monday 16:00 - 17:30 126Room #126 (Graduate School of Math. Sci. Bldg.) |
|---|---|
| Organizer(s) | Makiko Sasada, Shuta Nakajima (Keio Univ.), Masato Hoshino (Science Tokyo), Masahisa Ebina (Science Tokyo) |
Future seminars
2026/06/15
16:00-17:30 Room #126 (Graduate School of Math. Sci. Bldg.)
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Tomoyuki Ichiba (University of California Santa Barbara)
Feynman formula for discrete-time quantum walk and its applications
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Tomoyuki Ichiba (University of California Santa Barbara)
Feynman formula for discrete-time quantum walk and its applications
[ Abstract ]
We explicitly connect (discrete-time) quantum walks on Z with a four-state Markov additive process via a Feynman-type formula. Using this representation, we derive a relation between the spectral decomposition of the Markov additive process and the limiting density of the homogeneous quantum walk. In addition, we consider a space-time rescaling of quantum walks, which leads to a system of quantum transport PDEs of Dirac type in continuous time and space with phase interaction and potential terms. Our probabilistic representation for this type of PDE offers its stochastic extension as well as an efficient Monte Carlo computational technique. This is joint work with Jean-Pierre Fouque and Ka Lok Lam.
We explicitly connect (discrete-time) quantum walks on Z with a four-state Markov additive process via a Feynman-type formula. Using this representation, we derive a relation between the spectral decomposition of the Markov additive process and the limiting density of the homogeneous quantum walk. In addition, we consider a space-time rescaling of quantum walks, which leads to a system of quantum transport PDEs of Dirac type in continuous time and space with phase interaction and potential terms. Our probabilistic representation for this type of PDE offers its stochastic extension as well as an efficient Monte Carlo computational technique. This is joint work with Jean-Pierre Fouque and Ka Lok Lam.
2026/06/22
16:00-17:30 Room #126 (Graduate School of Math. Sci. Bldg.)
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Manasa Nagatsu (Kyoto University)
Large $N$ expansion for smooth multi-trace spectral statistics of
classical matrix ensembles, central limit theorems and matrix integrals.
We are having teatime from 15:15 in the common room on the second floor. Please join us.
Manasa Nagatsu (Kyoto University)
Large $N$ expansion for smooth multi-trace spectral statistics of
classical matrix ensembles, central limit theorems and matrix integrals.
[ Abstract ]
We consider expectations of the form $E [tr h_1(X_1^N)... tr h_r(X_r^N)]$,
where $X_i^N$ are self-adjoint polynomials in various independent
classical random matrices and $h_i$ are smooth test function and obtain a
large $N$ expansion of these quantities, building on the framework of
polynomial approximation and Bernstein-type inequalities recently
developed by Chen, Garza-Vargas, Tropp, and van Handel.
As applications of the above, we prove the higher-order asymptotic
vanishing of cumulants for smooth linear statistics, establish a Central
Limit Theorem, and demonstrate the existence of formal asymptotic
expansions for the free energy and observables of matrix integrals with
smooth potentials.
In addition to presenting these results, we will briefly review the role
of linear statistics in random matrix theory and discuss the motivation
behind the large $N$ expansion framework introduced in the context of
strong convergence.
This talk is based on joint work with Benoit Collins.
We consider expectations of the form $E [tr h_1(X_1^N)... tr h_r(X_r^N)]$,
where $X_i^N$ are self-adjoint polynomials in various independent
classical random matrices and $h_i$ are smooth test function and obtain a
large $N$ expansion of these quantities, building on the framework of
polynomial approximation and Bernstein-type inequalities recently
developed by Chen, Garza-Vargas, Tropp, and van Handel.
As applications of the above, we prove the higher-order asymptotic
vanishing of cumulants for smooth linear statistics, establish a Central
Limit Theorem, and demonstrate the existence of formal asymptotic
expansions for the free energy and observables of matrix integrals with
smooth potentials.
In addition to presenting these results, we will briefly review the role
of linear statistics in random matrix theory and discuss the motivation
behind the large $N$ expansion framework introduced in the context of
strong convergence.
This talk is based on joint work with Benoit Collins.
